Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.)
| Autor: | Hongxin Cao, Bo Huang, WenYu Zhang, Lei Xu, Ji'An Xia, Yuwang Yang, Daokuo Ge, Qian Wan, Weixin Zhang, Ke Yaqi |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Contextual image classification business.industry Hyperspectral imaging Forestry Pattern recognition 04 agricultural and veterinary sciences Horticulture Quadratic classifier 01 natural sciences Computer Science Applications VNIR Support vector machine Region of interest 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Artificial intelligence business Agronomy and Crop Science 010606 plant biology & botany Waterlogging (agriculture) Multivariate classification Mathematics |
| Zdroj: | Computers and Electronics in Agriculture. 159:59-68 |
| ISSN: | 0168-1699 |
| DOI: | 10.1016/j.compag.2019.02.022 |
| Popis: | The objective of this study was to investigate the utility of hyperspectral images (HSIs) for the detection of oilseed rape waterlogging stress. We assessed two oilseed rape varieties, the non-hybrid NingYou 22 (NY 22) and hybrid NingZa 19 (NZ 19), and HSIs of oilseed rape leaves under different durations of waterlogging stress (0, 3, and 6 days) were collected to build three datasets (NY 22, NZ 19, and both combined). We extracted red–green–blue (RGB) images and visible and near-infrared (VNIR 400–1000 nm) spectra from a region of interest (ROI) in each HSI. Quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), and support vector machine (SVM) classifiers were used to build classification models for comparing images and spectra of samples under different waterlogging levels among the three datasets, and to conduct training and prediction. From each dataset, 70% of the images were used for training, and the remaining 30% were used for testing. In the classification of full-wavelength HSIs (400–1000 nm), QDA and SVM exhibited high multivariate classification accuracy, reaching 77.37% and 95.90% accuracy, respectively. In contrast, KNN displayed low accuracy, but good identification and prediction ability for variety NZ 19. Six optimal wavebands of 529, 641, 698, 749, 856, and 979 nm were used as input for successive projections algorithm (SPA) classification and analysis. The QDA mode had better classification performance, with identification accuracies of 100% and 94.44%, respectively. Overall, the VNIR classification results exceeded those of image classification. These results show that hyperspectral imaging technology is feasible and useful for the detection of oilseed rape waterlogging stress. |
| Databáze: | OpenAIRE |
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